Collaborative Research: A Big Data Approach to Fundamental Paleoclimate Questions

该项目寻求应用大数据方法，利用新出现的古气候数据标准，解决气候动力学中的两个基本问题：(1)水气候突变的空间范围；(2)了解过去的温度变化如何有助于减少21世纪气候预测的传播。潜在的更广泛的影响包括在全球古气候界建设大数据分析能力。所有的分析将作为开源的计算叙述分享，以在职业生涯早期的研究人员中传播古数据科学的最佳实践，并为未来此类分析提供模板。具体地说，研究人员将利用最近的古气候数据合成努力，以及在分析按时间不确定的时间序列方面的进展，探索全新世古气候学的基本问题。这项拟议的工作还将把古气候数据与耦合模式比较项目(CMIP6)整体的未来气候预测联系起来。该项目将根据作为这项研究的一部分制定的工作流程和方法，每年举办三次数据管理和分析讲习班，为早期职业科学家提供培训。此外，该项目将支持一名研究生在古气候学、数据科学和气候建模的交叉点工作。其他更广泛的影响包括通过青年研究人员计划和知识驱动的跨学科数据科学中心进行推广，让数据科学领域的高中生和研究生接触跨学科研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project seeks to apply a Big Data approach, harnessing emerging standards for paleoclimate data, to address two fundamental questions in climate dynamics: (Q1) the spatial extent of abrupt changes in hydroclimate; and (Q2) How can knowledge of past temperature variations help reduce the spread of twenty-first century climate projections. The potential Broader Impacts include building capacity in Big Data analysis in the global paleoclimate community. All analyses will be shared as open-source computational narratives to disseminate best practices in paleo data science among early-career researchers, and to provide a template for future analyses of this kind.Specifically, the researchers will leverage recent paleoclimate data synthesis efforts, as well as advances in the analysis of chronologically uncertain timeseries, to explore fundamental questions in Holocene paleoclimatology. The proposed work will also link paleoclimate data to future climate projections from the Coupled Model Intercomparison Project (CMIP6) ensemble. The project will provide training to early career scientists through three yearly workshops in data management and analysis based on workflows and methodology developed as part of this research. Additionally, the project will support one graduate student working at the intersection between paleoclimatology, data science, and climate modeling. Other Broader Impacts include outreach through the Young Researchers program and Center for Knowledge-Powered Interdisciplinary Data Science to expose high-school students and graduate students in data science to interdisciplinary research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.